Source code for mindspore.common.initializer

# Copyright 2020 Huawei Technologies Co., Ltd
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"""Initializer for cell parameters."""
import numbers
import math

from functools import reduce
import numpy as np
from scipy.stats import truncnorm
from mindspore import log as logger

from . import dtype as mstype
from .tensor import Tensor

_INITIALIZER_ALIAS = dict()


[docs]class Initializer: """ The base class of the initializer. Args: kwargs (dict): Keyword arguments for Initializer. Returns: Array, assigned array. """ def __init__(self, **kwargs): self._kwargs = kwargs self.shape = None self.dtype = None def _initialize(self, *kwargs): raise NotImplementedError('Must be overridden!') def __call__(self, arr): return self._initialize(arr) @property def shape(self): return self._shape @shape.setter def shape(self, shape): self._shape = shape @property def dtype(self): return self._dtype @dtype.setter def dtype(self, dtype): self._dtype = dtype
[docs] def to_tensor(self, slice_index=None, shape=None): """ Get the tensor format data of this Initializer. Args: slice_index (int): Slice index of a parameter's slices. Used when initialize a slice of a parameter, it guarantee that devices use the same slice can generate the same tensor. shape (list[int]): Shape of the slice, used when initialize a slice of the parameter. """ arr = None if shape is None: shape = self.shape try: arr = np.ndarray(shape) except ValueError: msg = "Error shape={}".format(shape) logger.error(msg) raise ValueError(msg) if slice_index is not None: np.random.seed(slice_index) self.__call__(arr) return Tensor(arr, dtype=self.dtype)
def _register(*aliases): """Return the alias register.""" def alias_reg(cls): name = cls.__name__ name = name.lower() if name not in _INITIALIZER_ALIAS: _INITIALIZER_ALIAS[name] = cls for alias in aliases: if alias not in _INITIALIZER_ALIAS: _INITIALIZER_ALIAS[alias] = cls return cls return alias_reg def _assignment(arr, num): """Assign the value of `num` to `arr`.""" if arr.shape == (): arr = arr.reshape((1)) arr[:] = num arr = arr.reshape(()) else: if isinstance(num, np.ndarray): arr[:] = num[:] else: arr[:] = num return arr
[docs]@_register('zeros') class Zero(Initializer): """ Initialize the array to zero. Args: arr (Array): The array to be assigned. Returns: Array, assigned array. """ def _initialize(self, arr): _assignment(arr, 0)
[docs]@_register('ones') class One(Initializer): """ Initialize the array to one. Args: arr (Array): The array to be assigned. Returns: Array, assigned array. """ def _initialize(self, arr): _assignment(arr, 1)
def _calculate_in_and_out(arr): """ Calculate n_in and n_out. Args: arr (Array): Input array. Returns: Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`. """ dim = len(arr.shape) if dim < 2: raise ValueError("If initialize data with xavier uniform, the dimension of data must be greater than 1.") n_in = arr.shape[1] n_out = arr.shape[0] if dim > 2: counter = reduce(lambda x, y: x * y, arr.shape[2:]) n_in *= counter n_out *= counter return n_in, n_out
[docs]@_register('xavier_uniform') class XavierUniform(Initializer): r""" Initialize the array with xavier uniform algorithm, and from a uniform distribution collect samples within U[-boundary, boundary] where :math:`boundary = gain * \sqrt{\frac{6}{n_{in} + n_{out}}}`. Args: gain (Array): The array to be assigned. Default: 1. Returns: Array, assigned array. """ def __init__(self, gain=1): super(XavierUniform, self).__init__(gain=gain) self.gain = gain def _initialize(self, arr): n_in, n_out = _calculate_in_and_out(arr) boundary = self.gain * math.sqrt(6.0 / (n_in + n_out)) data = np.random.uniform(-boundary, boundary, arr.shape) _assignment(arr, data)
[docs]@_register('he_uniform') class HeUniform(Initializer): r""" Initialize the array with He kaiming uniform algorithm, and from a uniform distribution collect samples within U[-boundary, boundary] where :math:`boundary = \sqrt{\frac{6}{n_{in}}}` where :math:`n_{in}` is the number of input units in the weight tensor. Args: arr (Array): The array to be assigned. Returns: Array, assigned array. """ def _initialize(self, arr): n_in, _ = _calculate_in_and_out(arr) boundary = math.sqrt(6.0 / n_in) data = np.random.uniform(-boundary, boundary, arr.shape) _assignment(arr, data)
[docs]class Constant(Initializer): """ Initialize a constant. Args: value (Union[int, numpy.ndarray]): The value to initialize. Returns: Array, initialize array. """ def __init__(self, value): super(Constant, self).__init__(value=value) self.value = value def _initialize(self, arr): _assignment(arr, self.value)
[docs]@_register() class Uniform(Initializer): """ Initialize a uniform array, and obtain values U(-scale, scale) from the uniform distribution to fill the input tensor. Args: scale (float): The scale of the array. Default: 0.07. Returns: Array, uniform array. """ def __init__(self, scale=0.07): super(Uniform, self).__init__(scale=scale) self.scale = scale def _initialize(self, arr): tmp = np.random.uniform(-self.scale, self.scale, arr.shape) _assignment(arr, tmp)
[docs]@_register() class Normal(Initializer): """ Initialize a normal array, and obtain values N(0, sigma) from the uniform distribution to fill the input tensor. Args: sigma (float): The sigma of the array. Default: 0.01. Returns: Array, normal array. """ def __init__(self, sigma=0.01): super(Normal, self).__init__(sigma=sigma) self.sigma = sigma def _initialize(self, arr): tmp = np.random.normal(0, self.sigma, arr.shape) _assignment(arr, tmp)
[docs]@_register() class TruncatedNormal(Initializer): """ Initialize a truncated normal distribution which is a bounded normal distribution within N(low, high). Args: sigma (float): The sigma of the array. Default: 0.01. Returns: Array, truncated normal array. """ def __init__(self, sigma=0.01): super(TruncatedNormal, self).__init__(sigma=sigma) self.sigma = sigma def _initialize(self, arr): tmp = truncnorm.rvs(-2, 2, loc=0, scale=self.sigma, size=arr.shape, random_state=None) _assignment(arr, tmp)
[docs]def initializer(init, shape=None, dtype=mstype.float32): """ Create and initialize a tensor. Args: init (Union[Tensor, str, Initializer, numbers.Number]): Initialize value. - `str`: The `init` should be the alias of the class inheriting from `Initializer` and the corresponding class will be called. - `Initializer`: The `init` should be the class inheriting from `Initializer` to initialize tensor. - `numbers.Number`: The `Constant` will be called to initialize tensor. shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of output. Default: None. dtype (:class:`mindspore.dtype`): The type of data in initialized tensor. Default: mindspore.float32. Returns: Union[Tensor, Initializer], When `init` is Tensor, the return is Tensor object, otherwise the return is Initialize object. Examples: >>> tensor = initializer('ones', [1, 2, 3], mindspore.float32) """ if not isinstance(init, (Tensor, numbers.Number, str, Initializer)): raise TypeError("Unsupported init type '{}'.".format(type(init))) if isinstance(init, Tensor): init_shape = init.shape shape = shape if isinstance(shape, (tuple, list)) else [shape] if shape is not None and init_shape != tuple(shape): raise ValueError("The shape of init should be same as variable shape, but got the shape of init {} and " "the variable shape {}.".format(list(init.shape), shape)) return init if isinstance(shape, list): shape = tuple(shape) elif isinstance(shape, numbers.Number): shape = (shape,) if isinstance(init, Initializer): init.shape = init.shape if init.shape is not None else shape init.dtype = init.dtype if init.dtype is not None else dtype return init if isinstance(init, str): init_obj = _INITIALIZER_ALIAS[init.lower()]() if init_obj is None: raise ValueError("The class corresponding to '{}' was not found.".format(init)) init = init_obj init.shape = shape init.dtype = dtype return init if isinstance(init, numbers.Number): init_obj = Constant(init) init_obj.shape = shape init_obj.dtype = dtype return init_obj raise TypeError("Unsupported init type '{}'.".format(type(init)))
__all__ = [ 'Initializer', 'initializer', 'TruncatedNormal', 'Normal', 'Uniform', 'HeUniform', 'XavierUniform', 'One', 'Zero', 'Constant']